Fractional depletion estimation for battery condition metrics

ABSTRACT

A system is provided that evaluates an energy state. The system comprises a sensor, bins, and a processor. The sensor is communicatively coupled to at least one cell that is powering an electric load. The sensor is operative to collect samples, each representing a measure of current discharged from the at least one cell during a discharge event. The bins are stored in a memory, and are accessible by the processor. The processor is programmed to sort the collected samples into bins based upon sample value. The processor is also programmed to create a use estimate based upon samples sorted into their corresponding bins. Yet further, the processor is programmed to determine a fractional depletion for each bin, each fractional depletion being a quotient that is computed by dividing an expected value for that bin by the use estimate for that bin.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/296,353, filed Oct. 18, 2016, entitled FRACTIONAL DEPLETIONESTIMATION FOR BATTERY CONDITION METRICS, now allowed, which is acontinuation of U.S. patent application Ser. No. 14/208,214, filed Mar.13, 2014, entitled FRACTIONAL DEPLETION ESTIMATION FOR BATTERY CONDITIONMETRICS, issued as U.S. Pat. No. 9,488,697 on Nov. 8, 2016, which claimsthe benefit of U.S. Provisional Patent Application Ser. No. 61/792,907,filed Mar. 15, 2013, entitled FRACTIONAL DEPLETION ESTIMATION FORBATTERY CONDITION METRICS, the disclosures of which are herebyincorporated by reference.

BACKGROUND

The present disclosure relates in general to battery management, and inparticular, to the evaluation of a battery state based upon a fractionaldepletion estimation.

Industrial batteries represent a significant operating cost foroperators of fleets of industrial vehicles. In this regard, operatingcost is realized in both servicing the battery (e.g., recharging thebattery, performing maintenance on the battery, etc.) and in replacementof a worn out battery.

Lead-acid batteries represent a predominant type of industrial battery,especially for electrically operated vehicles such as forklift trucks.However, despite over one hundred years of proven reliability in use andrelatively low acquisition cost per kilowatt hour, lead-acid batteries,like all batteries still require service and eventual replacement.

BRIEF SUMMARY

According to various aspects of the present disclosure, a method ofevaluating battery state is provided. The method comprises collecting,at a sampling interval, samples of a measure of current discharged froma battery powering an electric load during a discharge event. The methodalso comprises sorting the collected samples into bins so as to groupthe collected samples based upon sample value. Additionally, the methodcomprises integrating, for each bin, the samples in that bin to create abattery use estimate based on a number of samples sorted into the bin, avalue associated with the bin, and the sampling interval. The methodstill further comprises determining, for each bin, a fractionaldepletion contribution of the battery based on the battery use estimateand an expected value for that bin. Moreover, the method comprisesgenerating a depletion estimate associated with the state of the batterybased upon an accumulation of the fractional depletion contributions.

According to further aspects of the present disclosure, a method ofevaluating an energy state is provided. The method comprises collecting,at a sampling interval, samples of a measure of current discharged fromat least one cell powering an electric load during a discharge event.The method also comprises sorting the collected samples into bins so asto group the collected samples based upon sample value. Additionally,the method comprises integrating, for each bin, the samples in that binto create a use estimate based on a number of samples sorted into thebin, a value associated with the bin, and the sampling interval. Themethod still further comprises determining, for each bin, a fractionaldepletion contribution of the at least one cell based on the useestimate and an expected value for that bin. Moreover, the methodfurther comprises generating a depletion estimate associated with thestate of the at least one cell based upon an accumulation of thefractional depletion contributions.

According to yet further aspects of the present disclosure, a system isprovided that evaluates a state of a battery. The system comprises asensor, bins, and a processor. The sensor is communicatively coupled toa battery that is powering an electric load. Here, the sensor isoperative to collect samples, where each sample represents a measure ofcurrent discharged from the battery during a discharge event. The binsare stored in memory and are accessible by the processor. The processoris programmed to sort the collected samples into bins so as to group thecollected samples based upon sample value. The processor is alsoprogrammed to integrate, for each bin, the samples in that bin to createa battery use estimate based on a number of samples sorted into the bin,a value associated with the bin, and the sampling interval. Theprocessor is further programmed to determine, for each bin, a fractionaldepletion contribution of the battery based on the battery use estimateand an expected value for that bin. Moreover, the processor isprogrammed to generate a depletion estimate associated with the state ofthe battery based upon an accumulation of the fractional depletioncontributions.

According to still further aspects of the present disclosure, a systemis provided that evaluates an energy state. The system comprises asensor, bins, and a processor. The sensor is communicatively coupled toat least one cell that is powering an electric load. Here, the sensor isoperative to collect samples, where each sample represents a measure ofcurrent discharged from the at least one cell during a discharge event.The bins are stored in a memory, and are accessible by the processor.The processor is programmed to sort the collected samples into bins soas to group the collected samples based upon sample value. The processoris also programmed to create a use estimate based upon samples sortedinto their corresponding bins. Yet further, the processor is programmedto determine a fractional depletion for each bin, each fractionaldepletion being a quotient that is computed by dividing an expectedvalue for that bin by the use estimate for that bin.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic of an illustrative operating environment in whichaspects of the disclosure herein may be practiced;

FIG. 2 is a view of an operator in an industrial vehicle, where theindustrial vehicle includes a battery, a battery management system andan information linking device for wirelessly communicating with acomputing environment, according to aspects of the present disclosure;

FIG. 3 is a flow chart of a method for performing a fractional depletionestimation, according to aspects of the present disclosure;

FIG. 4 is a representation of an exemplary load history that may be usedto compute a fractional depletion estimation, according to aspects ofthe present disclosure;

FIG. 5 is a representation of an exemplary current domain spectrum thatmay be used to compute a fractional depletion estimation, according toaspects of the present disclosure;

FIG. 6 is a representation of an exemplary curve that may be used tocompute a fractional depletion estimation, according to aspects of thepresent disclosure; and

FIG. 7 is a diagram of an exemplary computer processing system forimplementing the methods and processes described more fully herein.

DETAILED DESCRIPTION

According to various aspects of the present disclosure, approaches areprovided for evaluating a battery state, e.g., for estimating effectssuch as the damaging or depleting effects of a discharge load history onidentified battery condition metrics. In this regard, the approachesdescribed herein are useful for estimating health-based aspects ofbatteries. In particular, the approaches herein are well suited forevaluating a battery state of lead-acid batteries typically used inindustrial vehicles.

As will be described in greater detail herein, a battery metric may beestimated for a battery under evaluation by transforming a time-basedhistory of the load (e.g., current draw vs. time) on the battery into aspectral representation of that history in a “load” domain. Forinstance, in an example of current draw vs. time, the spectralrepresentation may be implemented in a load domain that characterizescurrent (e.g., current vs. amp-hours discharged). With the load historytransformed to a load domain, the evaluation comprises comparing thespectral representation to an expected battery capability, e.g., ametric of the battery condition. For a discrete spectrum, the comparisonis implemented for the load represented by each line in the spectrum.The individual contributions of each line can be summed to compute anoverall measure.

For instance, a measure of depleted amp-hours at a given magnitude ofcurrent draw can be compared to a curve, such as a plot of lifetimeampere-hour throughput expectation vs. discharge rate. The evaluationthen calculates the fraction of the expected capability used at eachspectral line, and aggregates those fractions into a total fraction thatrepresents an estimate of the expected capability ‘consumed by,’‘depleted by’ or otherwise associated with that particular time history.In this manner, techniques are provided to assess the cumulative effect,e.g., damage, depletion, consumption or other measurable metricassociated with the battery. For instance, the determined cumulativeeffect on a battery for an industrial vehicle can be utilized toestimate the lifetime, remaining life expectancy, amount of batteryconsumed or otherwise depleted, etc.

System Architecture

Aspects of the present disclosure comprise systems that enableindustrial vehicles to wirelessly communicate with applications deployedin an enterprise computing environment. As used herein, an industrialvehicle is any equipment that is capable of moving or otherwise beingmoved about a work site. Exemplary industrial vehicles include materialshandling vehicles, such as forklift trucks, reach trucks, turret trucks,walkie stacker trucks, tow tractors, hand operated pallet trucks, etc.

Referring now to the drawings and particularly to FIG. 1, an exemplarycomputing environment 10 is illustrated, which includes components thatsupport wireless communication capabilities. A plurality of industrialvehicles 12, such as materials handling vehicles (shown as forklifttrucks for convenience of illustration), each include a communicationsdevice that enables that industrial vehicle 12 to wirelessly communicatewith a processing device, such as an industrial vehicle applicationserver 14. The industrial vehicle application server 14 may furtherinteract with a data resource 16, e.g., one or more databases, datastores or other sources of information, to facilitate interaction withthe industrial vehicles 12 as will be described in greater detailherein.

The computing environment 10 may further support additional processingdevices 18, which may comprise for example, servers, personal computers,etc. One or more of the processing devices 18 may also communicate withthe industrial vehicles 12 and/or the industrial vehicle applicationserver 14 across the computing environment 10.

The wireless communication architecture may be based upon a standardwireless fidelity (WiFi) infrastructure, such as may be deployed usingstandard 802.11.xx wireless networks for a communications protocol.However, any other suitable protocol may alternatively be implemented.In an exemplary WiFi implementation, one or more wireless access points20 may be utilized to relay data between a wireless transceiver of eachindustrial vehicle 12 and one or more wired devices of the computingenvironment 10, e.g., the industrial vehicle application server 14.

Moreover, the computing environment 10 may be supported by one or morehubs 22 and/or other networking components that interconnect the varioushardware and/or software processing devices, including for example,routers, firewalls, network interfaces and correspondinginterconnections. The particular networking components provided in thecomputing environment 10 may thus be selected to support one or moreintranets, extranets, local area networks (LAN), wide area networks(WAN), wireless networks (WiFi), the Internet, including the world wideweb, and/or other arrangements for enabling communication across thecomputing environment 10, either real time or otherwise, e.g., via timeshifting, batch processing, etc.

Also, one or more computing devices may further communicate with aremote server 30, such as across a network 32 such as the Internet. Theremote server 30 may comprise, for example, a third party server (e.g.,operated by the industrial vehicle manufacturer) that interacts with theindustrial vehicles 12, the industrial vehicle application server 14and/or other processing devices 18 of the computing environment(s) 10.The remote server 30 may further interact with a data resource 34, e.g.,one or more databases, data stores or other sources of information.

Industrial Vehicle Data Collection

Referring to FIG. 2, an industrial vehicle 12 includes a communicationdevice referred to herein as an information linking device 38, a battery40 comprised of a plurality of battery cells 42 and a battery monitor 44that allows monitoring of battery characteristics, e.g., current,voltage, resistance, temperature, water level, etc.

The information linking device 38 and other aspects of the industrialvehicle 12, as well as the corresponding computer environment such asthat described with reference to FIG. 1, can incorporate any of thefeatures and structures as set out in U.S. Pat. No. 8,060,400 toWellman, entitled “Fleet Management System”, the disclosure of which isincorporated by reference in its entirety.

Notably, the information linking device 38 can include a display, atransceiver for wireless communication, e.g., with the applicationserver 14, I/O, a processor, memory for storing collected data about thecorresponding industrial vehicle, etc., as described more fully in U.S.Pat. No. 8,060,400 to Wellman, entitled “Fleet Management System”.

In exemplary implementations, the information linking device 38 iscoupled to and/or communicates with other industrial vehicle systemcomponents via a suitable industrial vehicle network system, e.g., avehicle network bus. The industrial vehicle network system is any wiredor wireless network, bus or other communications capability that allowselectronic components of the industrial vehicle 12 to communicate witheach other. As an example, the industrial vehicle network system maycomprise a controller area network (CAN) bus, ZigBee, Bluetooth, LocalInterconnect Network (LIN), time-triggered data-bus protocol (TTP) orother suitable communication strategy. As will be described more fullyherein, utilization of the industrial vehicle network system enablesseamless integration of the components of the industrial vehicleinformation linking device into the native electronics includingcontrollers of the industrial vehicle 12 and optionally, any electronicsperipherals associated with the industrial vehicle 12 that integratewith and can communicate over the network system.

The battery monitor 44 communicates with the information linking device38, the information server 14 or both. Moreover, the battery monitor 44may be wired to the information linking device 38, or the batterymonitor 44 may communicate with the information linking device 38 usinga wireless technology such as Bluetooth, etc. The battery monitor 44 mayinclude a processor, memory and other electrical structures to implementbattery monitoring. Moreover, the battery monitor 44 may interface withvarious sensors, such as a current sensor, one or more temperaturesensors, a water level sensor, voltage sensor, etc. to sample batterycharacteristics of interest. The results can be stored on the batterymonitor itself, or the samples can be communicated to the informationlinking device 38 for processing, storage, forwarding to the informationserver 14, etc.

Battery Monitoring

The battery monitor 44 may implement the fractional depletion estimationtechniques as set out herein. In alternative implementations, thebattery monitor 44 cooperates with the information linking device 38 toimplement the fractional depletion estimation techniques as set outherein. Here, the battery monitor 44 may provide the samples and theinformation linking device 38 may perform the storage and analysis.Other configurations that share responsibility of sample collection,storage and processing between the battery monitor 44 and informationlinking device 38 are also within the spirit of the disclosure herein.In still further alternative implementations, the battery monitor 44,the information linking device 38, the information server 14 orcombinations thereof, cooperate to implement the fractional depletionestimation techniques as set out herein. For instance, the batterymonitor 44 may send information directly to a server computer, such asinformation server computer 14 for storage, processing and analysis. Inanother illustrative example, the information linking device 38 servesas an intermediate to pass battery information from the battery monitor44 to the information server computer 14, to manipulate information fromthe battery monitor 44 before forwarding the manipulated information tothe server 14, etc.

Fractional Depletion Estimation

The fractional depletion approaches herein can be understood withreference to FIG. 3, which illustrates a method 300 of evaluating abattery state, such as a current state of a battery characteristic. Thestate of a battery may comprise for example, battery state of health. Inthis regard, the method 300 can be utilized to determine a depletedcapacity of the battery state (e.g., depleted capacity of the batterywith regard to state of health as an illustrative example).

The method comprises collecting samples at 302, of an operatingcondition of a battery that is used to power an electric load. Forinstance, the method may implement sampling of the battery current flowfrom a battery used to power an electric load (e.g., a battery installedin an industrial vehicle as described with reference to FIGS. 1 and 2).The sampling at 302 provides a time-based history of the load on thebattery (e.g., current draw vs. time).

For instance, as described more fully with reference to FIGS. 1 and 2,an industrial vehicle 12 may include a battery monitor 44 that allowsthe current drawn by the industrial vehicle battery 40 to be sampledover time. In this regard, the collected samples can be stored on thebattery monitor 44 or on the information linking device 38. As yetanother example, the information linking device 38 or the batterymonitor 44 can be used to wirelessly communicate the collected sampledata to the industrial server 14 for storage and processing.

The sampling frequency for collecting samples from the battery monitor44 may be selected based on a number of factors, such as the electricdevice being powered by the battery, the storage available for storingsamples, the desired sampling resolution, etc. As an illustrativeexample, for a battery of an industrial vehicle, a sample rate ofbetween 1 Hz-10 Hz may be utilized to measure the current drawn from thebattery. Of course, other sampling rates may be utilized.

The method 300 also comprises determining at 304, fractional depletioncontributions of the collected samples to a state of the battery. Forinstance, the determination may be carried out by evaluating each sampleto determine a corresponding fractional depletion of the state of thebattery associated with each sample.

As an example, the determination at 304 may transform the samples from atime-based history of the load on the battery into a ‘spectral’representation of that history in a “load” domain. Where current draw issampled, the domain is designated as a “current” domain. In discreteimplementations, the spectral representation may have a spectracomprised of a plurality of spectral lines that represent the load,e.g., each spectral line may correspond to a value (or range of values)of current draw.

As will be described in greater detail herein, these spectral lines candefine bins that are used to sort the accumulated samples. Inillustrative implementations, the transformation can occur “on the fly”.For instance, each time a sample is collected, that sample may be binnedbased upon the value (e.g., magnitude) of that sample. Thus, a memoryaccumulates the sample values over time into bins, sorting the samplesbased upon value. As noted above, this memory may reside on the batterymonitor 44, the information linking device 38, server computer 14, (oranyplace else it is desirable to store the data). Alternatively, thesamples may be collected as a time series of values that are pinned at alater time.

The determination at 304 may also compare the spectral representation(the samples transformed into the load domain) to an expected batterycapability (e.g., lifetime amp-hours) for the load represented byspectral lines in the spectra. For instance, the spectral representationmay be compared to an expected battery capability for a certain metric(e.g., current) of the battery for each line in the spectralrepresentation. As will be described in greater detail herein, theexpected capability, e.g., lifetime amp-hours, may be expressed as acurve. The determination at 304 thus compares the sample contributionsof each load line against the curve to calculate the fraction of theexpected capability used at each spectral line. For instance, thedetermination may calculate a fraction of the expected batterycapability used at the spectral lines in the comparison.

The method 300 also comprises generating, at 306, a depletion estimateassociated with the state of the battery based upon an accumulation ofthe fractional depletion contributions. For instance, the method mayaggregate the calculated fractions into a total fraction that representsan estimate of the expected battery capability consumed, depleted,remaining, etc., which is associated with a particular time history.

Keeping with the above example, in illustrative implementations, themethod accumulates the fractional depletion contributions of eachspectral line (e.g., binned sample data) to derive an overall fractionaldepletion estimate, which is used to evaluate the state of the battery.

In illustrative implementations, the sampled battery data is aggregatedinto a load history (e.g., a set of bins) that grows over time, over thelifetime of the battery. In this case, the resulting total fractionaldepletion represents the historic use over the life of the battery. Inother examples, battery use can be captured in discrete load histories,where the various load histories are aggregated to represent thehistoric use over the life of the battery.

In other exemplary implementations, the time information of thecollected samples is preserved such that further or alternative analysiscan be run. For example, an analysis can be run on individual, timebounded load histories. This may be useful, for instance, to evaluatespecific situations, e.g., tracking the depletion for a given operationor set of operations.

The method 300 may also comprise making predictions or estimations at308, based upon the accumulated fractional contributions, as the stateof the battery.

For instance, the state of the battery may be identified by evaluatingthe depletion estimate and outputting a prediction of how much of abattery capacity has been depleted based upon the evaluation of thedepletion estimate. For instance, the prediction may simply identify theaccumulated depletion estimate computed at 306. Thus, the depletionestimate can be used to predict, for instance, “how much of a totalcapability has been used”. As an example using state of health, theprediction at 308 may indicate that X % of the expected battery life hasbeen depleted.

As another example, the state of the battery may be identified byevaluating the depletion estimate and outputting a prediction of howmuch of a battery capacity is remaining based upon the evaluation of thedepletion estimate. That is, the depletion estimate can be used topredict, for instance, “how much of a total capability is remaining”.Here, the computation may be (1−X=Y) where X is the fractional depletionestimate computed at 306. Keeping with the example using state ofhealth, the prediction at 308 may indicate that Y % of the expectedbattery life is remaining. In still alternative examples, a businessdecision may be reached to change a battery before the expected end oflife (e.g., at 95% expected life, thus making the prediction (0.95−X)where X is the depletion estimate computed at 306). Thus, a predictionbased upon remaining capacity is not limited to the comparison of thecomputed depletion estimate to 100% depletion of that capacity.

As yet another example, the state of the battery may be identified byevaluating the depletion estimate and outputting a prediction of aninterval until an occurrence of an event of interest related to thebattery state (such as state of health, state of charge, etc.), basedupon the evaluation of the depletion estimate. This estimation is “howmuch more work can be done” before the battery capability has beendepleted, a battery recharge is required, etc. The interval may bedetermined based upon time, energy used, or any other measurableparameter. Keeping with the above example of state of health, theprediction at 308 may indicate that the battery has Y months of lifeleft until the battery has been depleted. As another example, theprediction at 308 may indicate that a battery charge will be required“before the end of a shift”, “in the next two hours”, “after the nextfive picks”, etc. The prediction in this regard will likely requireadditional information, which can be derived from aggregated historicaldata (e.g., how much work a typical battery does over an interval).Alternatively, historical data related to the use of the particularbattery in question may be considered when making predictions. Forinstance, if a historical account indicates that a given batterydepletes about 1.667% of its life per month of use, then the system mayinfer that the battery will need to be replaced every five years. Inthis example, if the battery is four years old, and the depletionestimate computes to 80% depleted, the system can predict that thebattery will need to be replaced in one year.

The above predictions are not limited to state of health determinations.Moreover, other manipulations of the fractional depletion estimatecomputed at 304 are within the spirit of the invention.

As used herein, the state of health (SOH) refers generally to the wear,aging, etc., of a battery. Thus, the SOH can be used as a measure of theuseful life left in a battery. Notably, the various aspects of thepresent disclosure are not meant to be limited to SOH determinations.Rather, the approaches herein can be applied more generally to variousbattery metrics.

Sampling Current Draw

According to illustrative aspects of the present disclosure herein,collecting samples of an operating condition of a battery may beimplemented by sampling current drawn from the battery to accumulate aplurality of current samples.

In an illustrative implementation, the plurality of current samples iscollected into bins to group the samples based upon sample value. Forinstance, the samples may be collected into bins by collecting theplurality of current samples over a load history, defining a pluralityof bins, where each bin corresponds to at least one current value (e.g.,magnitude of the current), and accumulating the samples in the bins suchthat each current sample of the load history is accumulated into asingle bin based upon the value of the current sample.

The bins may be assigned through a bin allocation process that sets theminimum bin value, maximum bin value, number of bins, bin resolution,etc. In alternative implementations, the bin number, size, etc., ispredetermined and preconfigured. The determination of the bin number,size, spacing and other attributes may be based upon a number offactors. For instance, the bins may be defined such that each bincorresponds to at least one current value (but each bin may represent arange of current values), such that each current sample of the loadhistory is accumulated into a single bin based upon its data value. Thatis, each bin can be narrow, such as to hold samples of only a singlecurrent value, e.g., 400 amps. Alternatively, a bin can hold samplesthat fall within a range of current values, e.g., 400-410 amps.Moreover, when the evaluation is performed using a reasonably largehistory, the data values themselves can be evaluated to set the binminimum value, bin maximum value, bin resolution/spacing, etc.

Alternatively, there may be a certain level of domain knowledge thatshould allow an implementation to get “in the ballpark” without relyingupon a detailed history. Still further, the bin allocation may be fixedfor the life of the battery, or the bin allocation may be changed fromtime to time. For instance, it may be desirable to use historical datato scale/weight the bin selection process (possibly different resolutionas a function of current, etc.) to distribute the data in a desiredmanner.

In an illustrative implementation, battery state of health is to bedetermined. In this regard, the method 300 looks to determine thecumulative damage/fatigue that has occurred to the battery toestimate/predict remaining battery life, to predict the amount of lifealready consumed, or both. To do so, the method determines thefractional depletion contributions of each bin and accumulates thefractional depletion contributions into a depletion estimate byaccumulating the fractional contributions of the samples collected intoeach bin.

For instance, the determination at 304 may be implemented bytransforming the accumulated current samples so as to derive a useestimate for each bin. This may be accomplished by integrating acrosseach bin to determine discharged amp-hours represented by the bin. Theintegration may be performed by multiplying a count of the number ofsamples in a selected bin by a current value associated with the bintimes a sample interval used to collect the current samples.

This implementation further comprises identifying a curve thatcharacterizes battery lifetime amp-hours as a function of current. Theimplementation further compares the computed discharged amp-hours foreach bin to the identified curve, e.g., by computing a quotient for eachbin based upon the computed discharged amp-hours for that bin and amagnitude associated with a point on the curve identifying lifetimeamp-hours for that bin. Each computed quotient is accumulated, and theaccumulated total (depletion estimate) is used to predict an amount oflife of the battery used up by the load history.

The prediction at 308 may be performed “on-the-fly” or “off-line”. Forinstance, the prediction may be “off-line”, e.g., not performed in realtime. As an example, an operator interacting with data stored on theserver 14 may run a historical report based upon data collected on anindustrial vehicle, e.g., by the information linking device, and whichis wirelessly transmitted to the remote server. As another illustrativeexample, the prediction may be performed on a materials handling vehicleon-the-fly as samples are recorded by a processor of the materialshandling vehicle. Still further, the on-the-fly processing may beimplemented directly on the battery monitor 44.

Certain implementations may clear current samples from bins after beingwirelessly transmitted to a remote server. Alternative configurationsmay combine the evaluated bin fractional contributions of currentsamples collected over the life of the battery to evaluate the state ofthe battery.

Exemplary Fractional Depletion Estimation Techniques Overview

An illustrative method of evaluating a battery state may be implementedby collecting samples of battery current flow to generate currentsamples of a battery used to power an electric load. This illustrativemethod further comprises sorting the current samples into a plurality ofbins according to the respective values of the current samples anddetermining a fractional contribution to the state of the battery forsamples contained in the bins. For instance, the determination may beimplemented by evaluating at least some of the plurality of bins todetermine bin fractional depletion contributions to the state of thebattery made by accumulated current samples in the evaluated pluralityof bins, where the evaluation is based upon a comparison of theaccumulated samples to a curve that characterizes battery lifetimeamp-hours as a function of current.

This illustrative method further comprises evaluating each bin todetermine a bin fractional contribution to the state of the battery madeby any current samples contained in the bin and combining the evaluatedbin fractional depletion contributions into a depletion estimate toevaluate the state of the battery.

EXAMPLE

One aspect of the present invention relates to a method of evaluating abattery state, e.g., according to the method of FIG. 3, in terms of anestimation or prediction of the damage that has occurred to a batterydue to a discharge event. A fractional depletion estimation of batterylife based upon discharge can be generalized based upon the method ofFIG. 3, into the following process.

The process collects samples of an operating condition of a battery thatis used to power an electric load (e.g., analogous to 302 of FIG. 3). Inthis example, the operating condition is a measure of the currentflowing into and/or out of a battery (e.g., industrial vehicle battery).

The process also determines fractional depletion contributions of thecollected samples to a state of the battery, e.g., analogous to 304 ofFIG. 3. In this exemplary implementation, the samples are collected intoa “Load History”. The load history can be defined as any interval oftime over which samples are collected. In an example, the life of abattery is characterized in a single load history. Alternatively,consecutive load histories can be collected and stored. In thisimplementation, each load history need not be of the same length orinterval. Moreover, it is possible to simplify a load history to thetrivial case of a single sample.

The use of one or more load histories facilitates predicting anaccumulated depletion in a capacity related to the battery state, basedupon the depletion estimate for the load history, or based upon the loadhistory plus the fractional depletion estimates of previously collectedand aggregated load histories.

As noted in greater detail herein, the sample rate can be any reasonablesample rate. An exemplary sample rate is between 1 Hz-10 Hz.

The process then determines corresponding fractional depletioncontributions of the collected samples to a state of the battery. Forinstance, referring to FIG. 4, an exemplary load history 400 isillustrated. The exemplary load history 400 comprises a plurality ofsamples 402 that record the DC bus current in amperes as a function oftime.

The (time-based) load history is transformed into a spectralrepresentation in the ‘current domain.’ To transform the load historyinto a spectral representation of the sample data, the range of measuredcurrent sampled in the load history is divided into several spectralbands (bins). This can be conceptualized as a histogram of the samples,with the current on the axis of the abscissa.

As noted above, the minimum bin value and maximum bin value, range, binresolution, number of bins, bin size etc., may be determined based uponthe resolution of the desired result and based upon the nature of themetric under evaluation. Other factors to consider when setting theprocess parameters include the possible dynamic range of the currentrequirements/demand from the vehicle, the size of the battery, anddynamic sampling system constraints. In an exemplary case, the bin sizewas chosen as 2.5% of the 1 C-rate of the battery and the range waschosen as 0 to 120% of the 1 C-rate of the battery, resulting in a 25amp bin size and range of 0 to 1200 amps for the 1000 Ahr battery inthis example. Other bin sizes and ranges may be acceptable, dependingupon the application.

The process then transforms the accumulated current samples so as toderive a use estimate for each bin. In an illustrative example, a“frequency count” is compiled, i.e., count of the number of samples ineach band. The process converts the ‘frequency count’ at each bin levelto the amp hour (Ahr) discharged at that level, such as by performingsome form of integration on each band (bin), resulting in the ‘spectralplot’ of Ahr discharged vs. discharge rate.

A use estimate may comprise for instance, a measure of the Spectral BandAmp-hours (Ahr) discharged. Thus, in one example, simply stated, theintegrated value at each bin level is: Ahr=(# of counts)×(the bincurrent)×(sampling interval).

This formula may be adjusted, e.g., based on the specific applicationand preference for integration method (rectangular, trapezoidal, etc.)and the numerical technique used by the particular frequency counting orhistogram algorithm being used.

Referring to FIG. 5, a spectra 500 illustrates the use estimates 502plotted in the histogram. In the above-example, Spectral Band Amp-hours(Ahr) discharged at each bin are plotted on the axis of the ordinate.The discharge rate, expressed as current in amperes per 100 amp-hours,is plotted on the axis of the abscissa.

The process then compares each computed use estimate (e.g., SpectralBand Amp-hours (Ahr)) to a corresponding battery characteristic (e.g.,capability based upon a battery curve). An example curve represents abattery characteristic as a function of current, e.g., Lifetime Ahr as afunction of discharge amps. The process may then compute a fractionaldepletion (e.g., a total spectral band contribution) for each spectralband in the current domain, based upon the comparison with the batterycharacteristic represented in the curve.

For example, consider a plot of data, such as may be supplied by abattery manufacturer, which represents the expected battery life(expressed as lifetime Ahr throughput) as the metric of batterycondition expressed in the same load ‘domain’ as the spectral loadhistory plot. Other metrics of battery condition could also beconsidered. The process calculates the fraction of the expectedcapability used at each spectral line.

The process further generates a depletion estimate associated with thestate of the battery based upon an accumulation of the fractionaldepletion contributions, e.g., analogous to 306 of FIG. 3. For instance,the process aggregates the computed fractions at each spectral line intoa total fraction that represents the estimated fraction of the expectedcapability ‘consumed’ or ‘depleted’ by that particular time history.

In an exemplary implementation, the fraction at each spectral line isthe quotient of the magnitude of that spectral line (in some unit ofcharge, Ahr in this case) divided by the magnitude of the expectation atthe same discharge rate as the spectral line (expressed in the same unitof charge, Ahr in this case).

That is, the process compares for each bin, the corresponding useestimate for that bin with a corresponding point on the curve andcomputes therefrom, a fractional depletion estimate that estimates thefraction of the expected characteristic of the battery depleted by thecurrent samples in the bin.

Referring to FIG. 6, an exemplary curve 600 is illustrated. The curveplots lifetime amp/hours 602 as a function of discharge amps. Theprocess essentially matches each bin to its corresponding discharge ampsvalue on the abscissa of FIG. 6. The capability used for that bin iscompared to the lifetime Amp-hours of FIG. 6 to determine a fractionaldepletion.

For instance, for a SOH determination, each fractional depletion valuecorresponds to a fraction of the expected capability used at eachspectral line, e.g., a quotient computed by dividing the use estimate(e.g., Spectral Band Amp-hours in this case) by a corresponding point onthe curve (e.g., lifetime Ahr measure from the battery characteristic inthis example).

The fractions calculated for each spectral line are then summed. Thetotal fraction represents the estimated fraction of the expectedcapability that has been depleted. For example, if the expectedcapability is in units of lifetime Ahr throughput and the fraction is0.25, then it is estimated that 25% of the lifetime has been depleted bythe particular load history that was evaluated.

In broad terms, aspects of the present disclosure can be practiced‘off-line’ by operating on a recorded time history, or ‘on-line’ (or ‘onthe fly’) by sampling the current as it occurs on the vehicle andsubsequently ‘binning’ the counts and incrementing the spectral linesand carrying out the ‘Fractional Depletion’ estimation calculations.This allows the opportunity to display results on the industrialvehicle, or to process the results off-line, e.g., using a reportingprogram of the server 14.

For the technique to be used ‘on-line’ there is no requirement to save atime-domain load history. However, the process still needs to bin eachpoint and save the cumulative histogram. This could be viewed as a casewhere the length of the load history is only one (the last one) currentsample. As such, the ‘on-line’ approach may alternatively be considereda ‘recursive method’. This implementation is termed a “recursive method”because in the case of on-the-fly processing, the histogram continuouslybuilds upon itself.

The process then aggregates the computed fractional depletion values toderive a load history contribution. This is a measure of the depletioncontribution by the samples of the corresponding load history.Optionally, the process can further aggregate the load historycontribution to previously determined load history contributions todetermine a cumulative (e.g., lifetime aggregated value).

In an alternative implementation, a single histogram is maintained overthe life of the battery, where new samples are aggregated into theabove-defined computation. Moreover, the use of bins is presented forconvenience of explanation. In practice, the use of bins can be resolvedinto an algorithm so that a formulaic approach is implemented. However,the concepts are similar to that described above.

According to aspects of the present disclosure, each sample may beindependently saved. In this configuration, the system preserves a lotof flexibility for off-line processing. For instance, the informationserver 14 could define load histories in an ad-hoc manner at any giventime for “off-line” analysis. That is, the server 14 can go back and runhistorical reports that look at damage in terms of particular historicalperiods, events, etc. The server 14 could even dynamically redefine thedefinition of bin width because the process can rebuild the histogram asthe query dictates. However, this approach would cost in terms ofstorage requirements.

According to further aspects of the present disclosure, the system couldsave only the cumulative histogram. This allows the overall history tobe preserved at a significant savings in storage, but limits the queriesthat can be run on the data, and may require that the number of bins andthe size of each bin be predefined and fixed for the life of thebattery.

The questions of what data, how much data, and where to store the data,are questions that come up when defining requirements for implementationin a practical, affordable fashion on the industrial vehicle. The datastorage constraints in the battery module and bandwidth constraints whentransmitting data across the wireless network should be considered whenselecting a particular implementation.

According to illustrative implementations, a recursive method is carriedout on the battery module and the cumulative histogram and aggregatedSOH result are stored on the battery module (e.g., battery module 44described with reference to FIG. 2). Alternatively, data can be storedon an ‘event triggered’ basis where a time stamped ‘snapshot’ of thehistogram and the aggregated result are captured, buffered in themodule, and then sent to the server 14 via the information linkingdevice 38 for permanent storage. A number of other data elements beingsensed or calculated by the battery module can also be included in arecord, e.g., battery temperature, fluid level, voltage, resistance,electrolyte warning, calculated state of charge, etc.

The ‘event’ may comprise for instance, the transition from the chargingstate (as when the battery is connected to the charger) to thedischarging state (as when the battery is connected to the truck) orvice versa. This keeps storage size and data transmission to a minimum.Thus, the current SOH and cumulative load history can be obtained byquerying either the module or a cloud of records on the server. The‘spectral’ (histogram) history for any particular charge or discharge‘event’ can also be obtained from the server based on the time stampedhistogram records and some ‘delta’ calculations.

According to still further aspects of the present disclosure, the systemmay save only the aggregated result. This approach saves the most indata storage, but obviously reduces the types of queries that can be runagainst the data. If you do not save the cumulative histogram, youessentially reduce the approach to an algorithm.

Although described herein in general with regard to two-dimensionaldatasets, e.g., current and time, the disclosure is not so limited.Rather, any multi-dimensional data analysis may be implemented using thetechniques set out herein. As an illustrative example, in a previousexample, evaluating each current sample is implemented by determiningthe corresponding fractional contribution to the state of the batterybased on a curve of the battery (e.g., life amp-hours vs. time).However, the corresponding fractional contribution to the state of thebattery may be modified using an adjusting factor determined by anenvironmental parameter of the battery, such as temperature, age, etc.Alternatively, the curve may be a multi-dimensional curve, e.g.,plotting life amp-hours vs. time vs. temperature.

Referring to FIG. 7, a schematic of an exemplary computer system havingcomputer readable program code for executing aspects described hereinwith regard to the preceding FIGURES. The computer system can be in theserver computer 14, the information linking device 38, the batterymonitor 44, combinations thereof, etc.

The computer system 700 includes one or more microprocessors 710 thatare connected to memory 720 via a system bus 730. A bridge 740 connectsthe system bus 730 to an I/O Bus 750 that links peripheral devices tothe microprocessor(s) 710. Peripherals may include storage 760, such asa hard drive, removable media storage 770, e.g., floppy, flash, CDand/or DVD drive, I/O device(s) 780 such as a keyboard, mouse, etc. anda network adapter 790. The memory 720, storage 760, removable mediainsertable into the removable media storage 770 or combinations thereof,implement computer-readable hardware that stores machine-executableprogram code for implementing the methods, configurations, interfacesand other aspects set out and described herein.

Still further, the exemplary computer system may be implemented as anapparatus for evaluating a battery state, which may comprise a processor(e.g., microprocessor 710) coupled to a memory (e.g., memory 720,storage 760, removable media insertable into the removable media storage770 or combinations thereof), wherein the processor is programmed toevaluate a battery state by executing program code to perform one ormore of the methods set out herein.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice, e.g., the system described with reference to FIG. 7. Thus, acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves through a transmission media.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams. Each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmableinstruction execution apparatus, create a mechanism for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. It should also be noted that, in some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

These computer program instructions may also be stored in a computerreadable medium that when executed can direct a computer, otherprogrammable data processing apparatus, or other devices to function ina particular manner, such that the instructions when stored in thecomputer readable medium produce an article of manufacture includinginstructions which when executed, cause a computer to implement thefunction/act specified in the flowchart and/or block diagram block orblocks.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of anymeans or step plus function elements in the claims below are intended toinclude any disclosed structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present disclosure has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to the disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thedisclosure. The aspects of the disclosure herein were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure with various modifications as aresuited to the particular use contemplated.

Having thus described the disclosure of the present application indetail and by reference to embodiments thereof, it will be apparent thatmodifications and variations are possible without departing from thescope of the disclosure defined in the appended claims.

What is claimed is:
 1. A method of evaluating battery state comprising:collecting, at a sampling interval, samples of a measure of currentdischarged from a battery powering an electric load during a dischargeevent; sorting the collected samples into bins so as to group thecollected samples based upon sample value; integrating, for each bin,the samples in that bin to create a battery use estimate based on anumber of samples sorted into the bin, a value associated with the bin,and the sampling interval; determining, for each bin, a fractionaldepletion contribution of the battery based on the battery use estimateand an expected value for that bin; and generating a depletion estimateassociated with the state of the battery based upon an accumulation ofthe fractional depletion contributions.
 2. The method of claim 1 furthercomprising: outputting a measure of the battery state based upon thegenerated depletion estimate by outputting at least one of: a predictionof how much of a battery capacity has been depleted based upon theevaluation of the depletion estimate; a prediction of how much of abattery capacity is remaining based upon the evaluation of the depletionestimate; and a prediction of an interval until an occurrence of anevent of interest related to the battery state, based upon theevaluation of the depletion estimate.
 3. The method of claim 1, wherein:collecting, at a sampling interval, samples of a measure of currentcomprises collecting the samples over a load history; and sorting thecollected samples comprises accumulating each sample of the load historythat represent a measure of a magnitude of a current discharged from theindustrial vehicle battery into a corresponding bin based upon the valueof the current sample, where each bin stores only samples accumulatedduring the load history; further comprising outputting at least one of:a prediction of an accumulated depletion in a capacity related to thebattery state, based upon the depletion estimate for the load history;and a prediction of the accumulated depletion in the capacity based uponthe depletion estimate for the load history and fractional depletionestimates of previously collected and aggregated load histories.
 4. Themethod of claim 1, wherein: integrating, for each bin, the samples inthat bin to create a battery use estimate comprises integrating acrosseach bin to determine discharged amp-hours represented by the bin;determining, for each bin, a fractional depletion contributioncomprises: identifying a curve that characterizes battery lifetimeamp-hours as a function of current; and comparing each battery useestimate with an associated point on the curve by: computing a quotientfor each bin based upon the computed discharged amp-hours for that binand a magnitude associated with a point on the curve identifyinglifetime amp-hours for that bin; and computing therefrom, a fractionaldepletion estimate that estimates a fraction of the expectedcharacteristic of the battery depleted by the current samples in thecorresponding bin; and generating, by the processor, a depletionestimate by accumulating each computed quotient to predict an amount oflife of the battery used up by the load history.
 5. The method of claim1 further comprising: predicting an accumulated depletion in a capacityrelated to the battery state by performing the prediction on anindustrial vehicle on the fly as samples are recorded by a processor ofthe materials handling vehicle.
 6. A method of evaluating an energystate comprising: collecting, at a sampling interval, samples of ameasure of current discharged from at least one cell powering anelectric load during a discharge event; sorting the collected samplesinto bins so as to group the collected samples based upon sample value;integrating, for each bin, the samples in that bin to create a useestimate based on a number of samples sorted into the bin, a valueassociated with the bin, and the sampling interval; determining, foreach bin, a fractional depletion contribution of the at least one cellbased on the use estimate and an expected value for that bin; andgenerating a depletion estimate associated with the state of the atleast one cell based upon an accumulation of the fractional depletioncontributions.
 7. The method of claim 6 further comprising: outputting ameasure of the energy state based upon the generated depletion estimateby outputting at least one of: a prediction of how much of a capacityhas been depleted based upon the evaluation of the depletion estimate; aprediction of how much of a capacity is remaining based upon theevaluation of the depletion estimate; and a prediction of an intervaluntil an occurrence of an event of interest related to the energy state,based upon the evaluation of the depletion estimate.
 8. The method ofclaim 6, wherein: collecting, at a sampling interval, samples of ameasure of current comprises collecting the samples over a load history;and sorting the collected samples comprises accumulating each sample ofthe load history that represent a measure of a magnitude of a currentdischarged from the at least one cell into a corresponding bin basedupon the value of the current sample, where each bin stores only samplesaccumulated during the load history; further comprising outputting atleast one of: a prediction of an accumulated depletion in a capacityrelated to the energy state, based upon a depletion estimate for theload history; and a prediction of the accumulated depletion in thecapacity based upon the depletion estimate for the load history andfractional depletion estimates of previously collected and aggregatedload histories.
 9. The method of claim 6, wherein: integrating, for eachbin, the samples in that bin to create a use estimate comprisesintegrating across each bin to determine discharged amp-hoursrepresented by the bin; determining, for each bin, a fractionaldepletion contribution comprises: identifying a curve that characterizeslifetime amp-hours as a function of current; and comparing each useestimate with an associated point on the curve by: computing a quotientfor each bin based upon the computed discharged amp-hours for that binand a magnitude associated with a point on the curve identifyinglifetime amp-hours for that bin; and computing therefrom, a fractionaldepletion estimate that estimates a fraction of the expectedcharacteristic of the at least one cell depleted by the current samplesin the corresponding bin; and generating, by the processor, a depletionestimate by accumulating each computed quotient to predict an amount oflife of the at least one cell used up by the load history.
 10. Themethod of claim 6 further comprising: predicting an accumulateddepletion in a capacity related to the energy state by performing theprediction on an industrial vehicle on the fly as samples are recordedby a processor of the materials handling vehicle.
 11. A system thatevaluates a state of a battery, the system comprising: a sensorcommunicatively coupled to a battery that is powering an electric load,the sensor operative to collect samples, each sample representing ameasure of current discharged from the battery during a discharge event;bins stored in a memory and accessible by a processor; wherein theprocessor is programmed to: sort the collected samples into bins so asto group the collected samples based upon sample value; integrate, foreach bin, the samples in that bin to create a battery use estimate basedon a number of samples sorted into the bin, a value associated with thebin, and the sampling interval; determine, for each bin, a fractionaldepletion contribution of the battery based on the battery use estimateand an expected value for that bin; and generate a depletion estimateassociated with the state of the battery based upon an accumulation ofthe fractional depletion contributions.
 12. The system of claim 11,wherein the processor is further programmed to: output a measure of thebattery state based upon the generated depletion estimate that includesat least one of: a prediction of how much of a battery capacity has beendepleted based upon the evaluation of the depletion estimate; aprediction of how much of a battery capacity is remaining based upon theevaluation of the depletion estimate; and a prediction of an intervaluntil an occurrence of an event of interest related to the batterystate, based upon the evaluation of the depletion estimate.
 13. Thesystem of claim 11, wherein the processor is further programmed to:collect the samples over a load history; and sort the collected samplesthrough accumulation of each sample of the load history that represent ameasure of a magnitude of a current discharged from the industrialvehicle battery into a corresponding bin based upon the value of thecurrent sample, where each bin stores only samples accumulated duringthe load history; and output at least one of: a prediction of anaccumulated depletion in a capacity related to the battery state, basedupon a depletion estimate for the load history; and a prediction of theaccumulated depletion in the capacity based upon the depletion estimatefor the load history and fractional depletion estimates of previouslycollected and aggregated load histories.
 14. The system of claim 11,wherein the processor is further programmed to: integrate, for each bin,the samples in that bin to create a battery use estimate throughintegration across each bin to determine discharged amp-hoursrepresented by the bin; determine, for each bin, a fractional depletioncontribution by implementing code to: identify a curve thatcharacterizes battery lifetime amp-hours as a function of current;compute a quotient for each bin based upon the computed dischargedamp-hours for that bin and a magnitude associated with a point on thecurve identifying lifetime amp-hours for that bin; and computetherefrom, a fractional depletion estimate that estimates a fraction ofthe expected characteristic of the battery depleted by the currentsamples in the corresponding bin; and generate, by the processor, adepletion estimate by accumulating each computed quotient to predict anamount of life of the battery used up by the load history.
 15. Thesystem of claim 11, wherein the processor is further programmed to:predict an accumulated depletion in a capacity related to the batterystate by performing the prediction on an industrial vehicle on the flyas samples are recorded by a processor of the materials handlingvehicle.
 16. A system that evaluates an energy state, the systemcomprising: a sensor communicatively coupled to at least one cell thatis powering an electric load, the sensor operative to collect samples,each sample representing a measure of current discharged from the atleast one cell during a discharge event; bins stored in a memory andaccessible by a processor; wherein the processor is programmed to: sortthe collected samples into bins so as to group the collected samplesbased upon sample value; create a battery use estimate based uponsamples sorted into their corresponding bins; and determine a fractionaldepletion of the battery for each bin, each fractional depletion being aquotient that is computed by dividing an expected value for that bin bythe battery use estimate for that bin.
 17. The system of claim 16,wherein the processor is further programmed to: output a measure of theenergy state based upon the generated depletion estimate that includesat least one of: a prediction of how much of a cell capacity has beendepleted based upon the evaluation of the depletion estimate; aprediction of how much of a cell capacity is remaining based upon theevaluation of the depletion estimate; and a prediction of an intervaluntil an occurrence of an event of interest related to the energy state,based upon the evaluation of the depletion estimate.
 18. The system ofclaim 16, wherein the processor is further programmed to: collect thesamples over a load history; and sort the collected samples throughaccumulation of each sample of the load history that represent a measureof a magnitude of a current discharged from the at least one cell into acorresponding bin based upon the value of the current sample, where eachbin stores only samples accumulated during the load history; and outputat least one of: a prediction of an accumulated depletion in a capacityrelated to the energy state, based upon a depletion estimate for theload history; and a prediction of the accumulated depletion in thecapacity based upon the depletion estimate for the load history andfractional depletion estimates of previously collected and aggregatedload histories.
 19. The system of claim 16, wherein the processor isfurther programmed to: integrate, for each bin, the samples in that binto create a use estimate through integration across each bin todetermine discharged amp-hours represented by the bin; determine, foreach bin, a fractional depletion contribution by implementing code to:identify a curve that characterizes lifetime amp-hours as a function ofcurrent; compute a quotient for each bin based upon the computeddischarged amp-hours for that bin and a magnitude associated with apoint on the curve identifying lifetime amp-hours for that bin; andcompute therefrom, a fractional depletion estimate that estimates afraction of the expected characteristic of the at least one celldepleted by the current samples in the corresponding bin; and generate,by the processor, a depletion estimate by accumulating each computedquotient to predict an amount of life of the at least one cell used upby the load history.
 20. The system of claim 16, wherein the processoris further programmed to: predict an accumulated depletion in a capacityrelated to the energy state by performing the prediction on anindustrial vehicle on the fly as samples are recorded by a processor ofthe materials handling vehicle.